def sample_recommendation_user(model, interactions, user_id, user_dict, 
                               item_dict,threshold = 0,nrec_items = 5, show = True):
    
    n_users, n_items = interactions.shape
    user_x = user_dict[user_id]
    scores = pd.Series(model.predict(user_x,np.arange(n_items), item_features=books_metadata_csr))
    scores.index = interactions.columns
    scores = list(pd.Series(scores.sort_values(ascending=False).index))
    
    known_items = list(pd.Series(interactions.loc[user_id,:] \
                                 [interactions.loc[user_id,:] > threshold].index).sort_values(ascending=False))
    
    scores = [x for x in scores if x not in known_items]
    return_score_list = scores[0:nrec_items]
    known_items = list(pd.Series(known_items).apply(lambda x: item_dict[x]))
    scores = list(pd.Series(return_score_list).apply(lambda x: item_dict[x]))
    if show == True:
        print ("User: " + str(user_id))
        print("Known Likes:")
        counter = 1
        for i in known_items:
            print(str(counter) + '- ' + i)
            counter+=1
print("\n Recommended Items:")
        counter = 1
        for i in scores:
            print(str(counter) + '- ' + i)
            counter+=1